whisper-small-GL-EN
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3405
- Wer: 71.9621
- Bleu: 20.1999
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.25e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Bleu |
|---|---|---|---|---|---|
| 0.6816 | 1.0 | 236 | 1.6335 | 67.2612 | 22.2158 |
| 0.1904 | 2.0 | 472 | 1.7234 | 69.9647 | 21.0583 |
| 0.2177 | 3.0 | 708 | 1.8764 | 73.2720 | 19.0086 |
| 0.0334 | 4.0 | 944 | 2.0541 | 72.6774 | 19.7679 |
| 0.0129 | 5.0 | 1180 | 2.1722 | 70.6708 | 19.8076 |
| 0.011 | 6.0 | 1416 | 2.2637 | 71.2653 | 19.7416 |
| 0.0062 | 7.0 | 1652 | 2.3214 | 70.3920 | 20.3474 |
| 0.0067 | 8.0 | 1888 | 2.3405 | 71.9621 | 20.1999 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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Base model
openai/whisper-small